Alzheimer's Disease

DUNE: a versatile neuroimaging encoder captures brain complexity across three major diseases: cancer, dementia and schizophrenia

Magnetic resonance images (MRI) of the brain exhibit high dimensionality that pose significant challenges for computational analysis. While models proposed for brain MRIs analyses yield encouraging results, the high complexity of neuroimaging data hinders generalizability and clinical application. We introduce DUNE, a neuroimaging-oriented encoder designed to extract deep-features from multisequen...

Investigating the causal network of dementia by employing a causal discovery approach combined with natural language processing models

Comprehensively studying modifiable risk factors altogether to explore how they contribute to dementia mechanism is imperative for effective interventions. This study utilized natural language processing (NLP) models to pre-select candidate risk factors of dementia from 5,505 variables in the UK Biobank. We then took a holistic machine learning approach, fast causal inference in combination with m...

Leveraging functional annotations to map rare variants associated with Alzheimer’s disease with gruyere

The increasing availability of whole-genome sequencing (WGS) has begun to elucidate the contribution of rare variants (RVs), both coding and non-codin...

AgeNet-SHAP: An explainable AI approach for optimally mapping multivariate regional brain age and clinical severity patterns in Alzheimer’s disease

Age is a significant risk factor for mild cognitive impairment (MCI) and Alzheimer’s disease (AD) and identifying brain age patterns is critical for c...

Quality of Human Expert vs. Large Language Model Generated Multiple Choice Questions in the Field of Mechanical Ventilation

Mechanical ventilation (MV) is a critical competency in critical care training, yet standardized methods for assessing MV-related knowledge are lackin...

Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimer’s disease (AD) via data fusion and machine learning

Accurately predicting which individuals with mild cognitive impairment (MCI) will progress to Alzheimer’s disease (AD) can improve patient care. This ...

Patient-centered Evaluation of AI Answers to Genetic Counseling Questions

The growing use of large language models for health communication raises important questions about patient preferences, trust, and satisfaction with A...

Integration of Deep Learning Annotations with Functional Genomics Improves Identification of Causal Alzheimer’s Disease Variants

Genetic variants associated with Alzheimer’s disease (AD) through genome-wide association studies (GWAS) are challenging to interpret because most lie...

Machine Learning Classification of Mild Cognitive Impairment using Advanced Multi-Shell Diffusion MRI and CSF Biomarkers

Machine learning applied to neuroimaging can help with medical diagnosis and early detection by identifying biomarkers of subtle changes in brain stru...

Neuroimaging-AI endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders

Recent work leveraging artificial intelligence has offered promise to dissect disease heterogeneity by identifying complex intermediate brain phenotyp...

Modeling the Spread of Misfolded Proteins in Alzheimer’s Disease using Higher-Order Simplicial Complex Contagion

Neurodegenerative diseases are characterized by complex proteins misfolded that propagate within the brain. For instance, current findings highlight t...

AutoRADP: An Interpretable Deep Learning Framework to Predict Rapid Progression for Alzheimer’s Disease and Related Dementias Using Electronic Health Records

Alzheimer’s disease (AD) and AD-related dementias (ADRD) exhibit heterogeneous progression rates, with rapid progression (RP) posing significant chall...

MRI2PET: Realistic PET Image Synthesis from MRI for Automated Inference of Brain Atrophy and Alzheimer’s

Positron Emission Tomography (PET) scans are a crucial tool in the diagnosing and monitoring of a number of complex conditions, including cancer, hear...

Cross-Disorder Machine Learning Uncovers Schizophrenia Risk Variants Predictive of Alzheimer’s Disease

Alzheimer’s disease (AD) and Schizophrenia (SCZ) exhibit overlapping clinical features and biological mechanisms, but the extent of their shared genet...

Targeted Serum Metabolomic Profiling and Machine Learning Approach in Alzheimer’s Disease using the Alzheimer’s Disease Diagnostics Clinical Study (ADDIA) Cohort

Metabolic biomarkers can potentially be used for early diagnosis, prognostic risk stratification and/or early treatment and prevention of individuals ...

Genotyping TOMM40’523 Poly-T Polymorphisms Using Whole-Genome Sequencing

The TOMM40’523 poly-T repeat polymorphism (rs10524523), located in the TOMM40 gene and in linkage disequilibrium with APOE, has been associated with c...

Silencer variants are key drivers of gene upregulation in Alzheimer’s disease

Alzheimer’s disease (AD), particularly late-onset AD, stands as the most prevalent neurodegenerative disorder globally. Owing to its substantial herit...

Predicting Alzheimer’s Trajectory: A Multi-PRS Machine Learning Approach for Early Diagnosis and Progression Forecasting

Predicting the early onset of dementia due to Alzheimer’s Disease (AD) has major implications for timely clinical management and outcomes. Current dia...

Smartphone-based behavioral profiling for distinguishing Dementia with Lewy bodies from Alzheimer’s Disease

Dementia with Lewy bodies (DLB) is frequently misdiagnosed as Alzheimer’s disease (AD) due to overlapping clinical presentations. In this study, we ev...

Epigenetic signatures of regional tau pathology and cognition in the aging and pathological brain

Primary age-related tauopathy (PART) and Alzheimer’s disease (AD) share hippocampal phospho-tau (p-tau) pathology but differ in ß-amyloid burden and d...